Vertesia Events
Join us at industry-leading events where we explore the future of generative AI. Discover upcoming events and webinars, or browse past events to see how we’re driving generative AI innovation.
Past Events
Navigating the Generative AI Software Marketscape
In this webinar, experts from Deep Analysis and Vertesia break down the AI software vendor landscape to help you understand the capabilities of different AI tools and the role Generative AI (GenAI) platforms play in accelerating adoption.
Key Trends and Technologies for Real-World Success
This webinar explores the future of AI for enterprise applications, focusing on the trends, technologies, and best practices that will shape successful deployments. Learn how IT teams are bringing AI into production seamlessly, optimizing decision-making, and maximizing efficiency gains.
Challenges in AI Implementation: Overcoming Obstacles and Resistance
Hear from Ramesh Dontha and industry experts as as they discuss the most common obstacles faced by businesses when creating and enforcing an AI strategy, how to navigate these challenges for business success, and tackling resistance to your AI strategy.
The Journey to Delivering GenAI Value in the Enterprise
CMSWire and Vertesia surveyed senior IT leaders at enterprise organizations about their current state of GenAI adoption and integration. This webinar presents the results of that research, exploring where organizations are prioritizing AI solutions and offering tips on how to improve and quicken the implementation process.
Why Single Model GenAI Experimentation Is Over
Grant Spradlin, VP of Product at Vertesia, shares insights from a recent industry survey on LLM integration readiness. This webinar on demand explores the top reasons preventing LLM projects from moving forward and why very prepared teams believe that virtualized LLM is a game changer.
Why AI Projects Get Stuck in Experimentation and How to Get Them Out
Steve Nouri of GenAI Works and Grant Spradlin of Vertesia discuss why AI projects get stuck in experimentation, which areas of LLM integration are most painful, and how enterprise teams are solving these challenges.